





"""simple docstring"""






# using dfs for finding eulerian path traversal

def      __A   (      a_    :int ,							a_    :Dict ,							a_    :str ,							a_    :Optional[int]=None)		->		List[str]:
				__a			:    Any      							=	(path or []) + [u]
				for v in graph[u]:
								if visited_edge[u][v] is False:
												__a							,    __a			:    Union[str, Any]      							=	True, True
												__a			:    List[Any]      							=	dfs(a_ ,							a_ ,							a_ ,							a_)
				return path

def      __A   (      a_    :int ,							a_    :int)		->		Optional[int]:
				__a			:    Any      							=	0
				__a			:    Optional[int]      							=	-1
				for i in range(a_):
								if i not in graph.keys():
												continue
								if len(graph[i]) % 2 == 1:
												odd_degree_nodes += 1
												__a			:    int      							=	i
				if odd_degree_nodes == 0:
								return 1, odd_node
				if odd_degree_nodes == 2:
								return 2, odd_node
				return 3, odd_node

def      __A   (      a_    :List[str] ,							a_    :Tuple)		->		Tuple:
				__a			:    List[str]      							=	[[False for _ in range(max_node + 1)] for _ in range(max_node + 1)]
				__a							,    __a			:    Any      							=	check_circuit_or_path(a_ ,							a_)
				if check == 3:
								print('''graph is not Eulerian''')
								print('''no path''')
								return
				__a			:    Any      							=	1
				if check == 2:
								__a			:    str      							=	odd_node
								print('''graph has a Euler path''')
				if check == 1:
								print('''graph has a Euler cycle''')
				__a			:    Any      							=	dfs(a_ ,							a_ ,							a_)
				print(a_)

def      __A   (      )		->		List[str]:
				__a			:    List[Any]      							=	{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
				__a			:    Any      							=	{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
				__a			:    List[Any]      							=	{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
				__a			:    str      							=	{1: [2, 3], 2: [1, 3], 3: [1, 2]}
				__a			:    List[Any]      							=	{
				    1: [],
				    2: []
				    # all degree is zero
				}
				__a			:    Tuple      							=	10
				check_euler(a_ ,							a_)
				check_euler(a_ ,							a_)
				check_euler(a_ ,							a_)
				check_euler(a_ ,							a_)
				check_euler(a_ ,							a_)


if __name__ == "__main__":
		main()



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


